research-paper-writing

Automate literature review by searching, verifying, and organizing related work with Semantic Scholar and arxiv. This streamlines the ML research workflow, allowing efficient collection and citation management of relevant academic sources for research papers.

Design and analyze ML experiments using a structured pipeline that leverages scipy and numpy for data processing. This approach supports iterative refinement, helping validate paper claims through robust experiment design and scientific analysis.

Create publication-ready plots using matplotlib and SciencePlots within the research workflow. This allows generation of high-quality scientific visualizations directly aligned with experiment data for ML research papers.

The research paper writing pipeline operates on Linux and macOS platforms. It requires various ML research tools including semanticscholar, habanero, requests, scipy, numpy, matplotlib, and SciencePlots to function properly.

Draft and revise ML research papers using a structured pipeline that supports iterative refinement and collaboration. This automates the writing process, incorporating feedback loops to efficiently produce high-quality scientific manuscripts.